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DAS-C01 Reliable Exam Topics - Training DAS-C01 Materials

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    Amazon DAS-C01 Certification Exam is suitable for professionals at all levels of expertise, including data architects, data engineers, data analysts, and business intelligence professionals. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification is recognized globally and demonstrates a professional's ability to design and implement data analytics solutions using AWS services. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification also provides professionals with a competitive edge in the job market and opens up opportunities for career growth.

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    The AWS Certified Data Analytics - Specialty (DAS-C01) Exam is a certification exam offered by Amazon Web Services (AWS) that focuses on assessing the skills and knowledge of data professionals in designing and implementing AWS services to derive insights from data. DAS-C01 exam is designed to test the candidate's ability to use AWS services for data analysis, as well as their ability to understand and optimize data input and output.

    Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q122-Q127):

    NEW QUESTION # 122
    A financial services company needs to aggregate daily stock trade data from the exchanges into a data store.
    The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency.
    The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
    Which solution meets the company's requirements?

    • A. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
    • B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
    • C. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
    • D. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.

    Answer: A


    NEW QUESTION # 123
    A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
    Which approach can provide the visuals that senior leadership requested with the least amount of effort?

    • A. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
    • B. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.
    • C. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.
    • D. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

    Answer: B


    NEW QUESTION # 124
    A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner.
    Which solution meets these requirements?

    • A. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.
    • B. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.
    • C. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.
    • D. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

    Answer: B


    NEW QUESTION # 125
    A medical company has a system with sensor devices that read metrics and send them in real time to an Amazon Kinesis data stream. The Kinesis data stream has multiple shards. The company needs to calculate the average value of a numeric metric every second and set an alarm for whenever the value is above one threshold or below another threshold. The alarm must be sent to Amazon Simple Notification Service (Amazon SNS) in less than 30 seconds.
    Which architecture meets these requirements?

    • A. Use an Amazon Kinesis Data Analytics application to read from the Kinesis data stream and calculate the average per second. Send the results to an AWS Lambda function that sends the alarm to Amazon SNS.
    • B. Use an AWS Lambda function to read from the Kinesis data stream to calculate the average per second and sent the alarm to Amazon SNS.
    • C. Use an Amazon Kinesis Data Firehose deliver stream to read the data from the Kinesis data stream and store it on Amazon S3. Have Amazon S3 trigger an AWS Lambda function that calculates the average per second and sends the alarm to Amazon SNS.
    • D. Use an Amazon Kinesis Data Firehose delivery stream to read the data from the Kinesis data stream with an AWS Lambda transformation function that calculates the average per second and sends the alarm to Amazon SNS.

    Answer: A


    NEW QUESTION # 126
    A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
    The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
    Which actions should the data analyst take to resolve this issue? (Choose two.)

    • A. Increase the Kinesis Data Streams retention period to reduce throttling.
    • B. Choose partition keys in a way that results in a uniform record distribution across shards.
    • C. Customize the application code to include retry logic to improve performance.
    • D. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
    • E. Increase the number of shards in the stream using the UpdateShardCount API.

    Answer: B,E

    Explanation:
    https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/


    NEW QUESTION # 127
    ......

    Training DAS-C01 Materials: https://www.updatedumps.com/Amazon/DAS-C01-updated-exam-dumps.html